Modelling pedestrian trajectory patterns with Gaussian processes

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Volumn , Issue , 2009, Pages 1229-1234

  Ellis, David ^a   Sommerlade, Eric ^a   Reid, Ian ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; LONG TERM; MOTION PREDICTION; NON-PARAMETRIC MODEL; PARAMETRIC MODELS; PEDESTRIAN MOTION; PEDESTRIAN TRAJECTORIES; RELATIVE MOTION; SURVEILLANCE CAMERAS; TRAJECTORY DATA;

COMPUTER VISION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); MONTE CARLO METHODS; SECURITY SYSTEMS;

MODELS;

EID: 77953184611     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2009.5457470     Document Type: Conference Paper

References (11)

1. S. Ali and M. Shah. Floor fields for tracking in high density crowd scenes. In ECCV '08: Proceedings of the 10th European Conference on Computer Vision, pages 1-14, Berlin, Heidelberg, 2008. Springer-Verlag.
2. P. Baiget, E. Sommerlade, I. Reid, and J. Gonzàlez. Finding prototypes to estimate trajectory development in outdoor scenarios. In Proceedings of the First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS2008), September 2008.
3. D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603-619, May 2002.
4. W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, and S. Maybank. A system for learning statistical motion patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(9):1450-1464, Sept. 2006.
5. N. Johnson and D. Hogg. Learning the distribution of object trajectories for event recognition. Image and Vision Computing, 14(8):609-615, 1996. (Pubitemid 126360016)
6. J. Ko and D. Fox. GP-BayesFilters: Bayesian filtering using gaussian process prediction and observation models. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3471-3476, Sept. 2008.
7. D. Makris and T. Ellis. Path detection in video surveillance. Image and Vision Computing, 20:895-903, 2002.
8. D. Makris, T. Ellis, and J. Black. Bridging the gaps between cameras. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)., volume 2, pages II-205-II-210 Vol.2, June-2 July 2004.
9. J. Quiñonero Candela and C. E. Rasmussen. A unifying view of sparse approximate gaussian process regression. J. Mach. Learn. Res., 6:1939-1959, 2005.
10. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, December 2006.
11. E. Snelson and Z. Ghahrami. Local and global sparse gaussian process approximations. Artificial Intelligence and Statistics 11 (AISTATS), 2007.